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    Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data

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    Qahwaji_et_al_IJACSA.pdf (841.8Kb)
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    Publication date
    2018
    Author
    Nagem, Tarek A.M.
    Qahwaji, Rami S.R.
    Ipson, Stanley S.
    Wang, Z.
    Al-Waisy, Alaa S.
    Keyword
    Convolutional; Neural; Network; Deep; Learning; Solar; Flare; Prediction; Space; Weather insert
    Rights
    ©The Authors 2018. This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
    Peer-Reviewed
    Yes
    
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    Abstract
    Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.
    URI
    http://hdl.handle.net/10454/15683
    Version
    Published version
    Citation
    Nagem TAMH, Qahwaji R, Ipson S et al (2018) Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data. International Journal of Advanced Computer Science and Applications (IJACSA). 9(1): 492-498.
    Link to publisher’s version
    http://dx.doi.org/10.14569/IJACSA.2018.090168
    Type
    Article
    Collections
    Engineering and Informatics Publications

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